Summary  
This chapter covers how to use a merge function to combine two tabular data structures by matching rows on shared key columns, performing an inner join by default.

General domain of usage  
Data analysis

When working with tabular data in pandas, you often need to combine information from multiple sources. This is where **merging DataFrames** becomes essential. Merging allows you to bring together rows from different DataFrames based on shared columns, making it possible to analyze related data in a unified view. Whether you are combining customer records with order details or joining survey results with demographic information, understanding how to merge DataFrames is a foundational skill in data analysis.

import pandas as pd

# Create two simple DataFrames with a common column 'id'
df_left = pd.DataFrame({
    "id": [1, 2, 3],
    "name": ["Alice", "Bob", "Charlie"]
})

df_right = pd.DataFrame({
    "id": [2, 3, 4],
    "score": [85, 92, 78]
})

# Merge the DataFrames on the 'id' column
merged_df = pd.merge(df_left, df_right, on="id")

print(merged_df)

The `merge()` function in pandas is designed to combine two DataFrames by aligning the rows based on one or more key columns. The most common way to use `merge()` is by specifying the column(s) that exist in both DataFrames using the `on` parameter. In the example above, both DataFrames have an `"id"` column, so `pd.merge(df_left, df_right, on="id")` matches rows with the same `"id"` value. By default, `merge()` performs an **inner join**, meaning only rows with matching keys in both DataFrames are included in the result. The resulting DataFrame contains all columns from both sources, with only the matched rows preserved. This approach is similar to joining tables in SQL and is fundamental when combining datasets that share a common attribute.

Which statement best describes the default behavior of the `merge()` function in pandas?

Master advanced data manipulation techniques in pandas, focusing on combining and reshaping DataFrames for powerful data analysis workflows.

Explore pandas techniques for combining multiple DataFrames, including merging, joining, and concatenation, to create unified datasets for analysis.

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merge() basics